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AI for Business Intelligence | Data Development with AI
Rating: 3.8 out of 5(9 ratings)
68 students

AI for Business Intelligence | Data Development with AI

Data Development solutions with AI | AI tools to enhance Data development and Data Productivity
Created byTyrone Dugmore
Last updated 3/2025
English

What you'll learn

  • Define how AI tools help Data professionals
  • AI Prompt Engineering for Data, SQL and PowerBI
  • Business Intelligence Lifecycle and Boosting your productivity with AI
  • Using AI for Data Engineering
  • Using AI for Data Querying
  • Using AI for Reporting and Dashboards
  • Using AI to create diagrams

Course content

1 section • 19 lectures • 2h 16m total length

  • Introduction1:52

    Transform your data into strategic assets by applying ai-driven business intelligence and data development practices, including generating complex sql queries, defining requirements, and building testable data solutions.

  • Why This Course1:43
  • Data and Business bridging the gap11:41
  • Business Intelligence and Data Lifecycle6:42

    Define the business problem and requirements analysis, determine core measurements and data points, build data warehouses and visualizations, and apply generative ai for faster coding, synthetic data, and documentation.

  • What is AI Code assist for Data professionals12:17
  • Tooling for Data and Analytics3:10

    Explore essential tooling for data and analytics, including SQL Server, Visual Studio Code, Gemini, Copilot, Mermaid, Power BI, GitHub, and AI-assisted coding to build data warehouses and visualizations.

  • AI Prompt engineering Foundations6:34
  • Github Co-Pilot Activation for VS Code4:39
  • Gemini Code Assist Activation for VS Code3:41
  • Use Cases for AI in Data Development & Requirements12:40
  • Use Cases for AI in Data Development - Explain Code using AI10:40
  • Use Cases for AI in Data Development - SQL Code with AI15:00
  • Use Cases for AI in Data Development - SQL Code Part 27:18
  • Use Cases for AI in Data Development - SQL Code Part 33:39
  • Use Cases for AI in Data Development - Testing SQL with AI8:13
  • Use Cases for AI in Data Development - Insights and Analysis with AI6:06
  • Use Cases for AI - Reporting, Dashboards and Visualisations with AI11:34
  • Use Cases for AI in Data Development - Diagrams for Data with AI7:57

    Explore how AI tools like mermaid, copilot, and Gemini code assist create ER diagrams, UML, mind maps, and Gantt charts to visualize data development and the BI life cycle.

  • Conclusion1:07

Requirements

  • Basic Database and Reporting knowledge required, you can learn as you go along!

Description

This comprehensive training pack equips data professionals with the skills to leverage AI for enhanced business intelligence and data development solutions.

Learn how to bridge the gap between data and business needs, understand the data lifecycle within a BI context, and master AI-powered code assistance tools like GitHub Copilot and Google Gemini Code Assist.

Increase your efficiency, improve code quality, and deliver data-driven insights faster with the power of AI. Includes hands-on exercises and real-world examples.

This course covers:

  • Data and Business bridging the gap

  • Business Intelligence and Data Lifecycle

  • What is AI Code assist for Data professionals

  • Tooling for Data and Analytics

  • Prompt engineering Foundations

  • Co-Pilot & Gemini Code Assist usage

  • Use Cases for AI in Data Development

    • Requirements

    • Explain

    • Document

    • Build

    • Test

    • Insights and Analysis

    • Diagramming your Solutions

What you will learn :

  • Prompt engineering foundations

  • Responsible AI practices

  • Practical use cases for AI in data development (including requirements gathering, code generation, testing, analysis, and visualization),

  • Integration with tools like SQL Server, Visual Studio Code, and Power BI.

Through real-world examples and practical exercises, you'll discover how to:

  • Accelerate code completion and reduce debugging time.

  • Generate synthetic data for robust testing and development.

  • Automate documentation and improve code understanding.

  • Translate code between languages (e.g., SAS to SQL, Python).

  • Extract valuable insights and formulate business recommendations.

  • Develop data warehouse solutions using the Kimball methodology.

  • Create impactful Power BI dashboards and reports.

  • Design clear and informative diagrams for solution documentation.


Who this course is for:

  • Beginner to Intermediate Data Analysts, Data engineers and Data professionals. Data curious people will benefit from the content.

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